1. Field of the Invention
The present invention generally relates to a system for diagnosing medical conditions using a neural network trained from clinical data. While the present invention may be adapted for a variety of medical conditions, in one embodiment it may be used for the diagnosis of low back pain.
2. Description of the Prior Art
Low back pain (LBP) is one of the most frequent and most disabling health problems affecting our society, and its incidence appears to be increasing. It has been estimated that, in the United States and Great Britain, this complaint will affect 80% of the population at some point during their lifetime. In Sweden in a 10 year period, 1% of all workdays were lost annually because of low back conditions. The average sickness absence period was 36 days, which is quite similar to the 24 days for the United States and the 33 days for Great Britain. Forty percent of the workers affected with low back pain were disabled for less than one week, while 9.9% were disabled for more than six months. No other disease category was responsible for a greater number of days lost from work. Approximately 2.4 million Americans are disabled because of LBP disorders, the major cause of disability under the age of 45.
Although sophisticated diagnostic means have been developed, it has been estimated that in 80% of cases there is no obvious source of nociception. Furthermore, the relationships between abnormal radiological findings and low back complaints are highly inconsistent. In the wide majority of cases, low back pain is considered as mechanical or functional. Therefore, functional assessment may be considered as useful to investigate those low back troubles. Functional assessment has been used to differentiate between different types of non-specific low back troubles and to prescribe and follow up specific rehabilitation. Differentiate between non-specific back troubles and specific pathologies in basic low back pain screening has also been used before going to more sophisticated and expensive investigation techniques (CT scan, MRI, etc.). In addition many researchers show positive findings in asymptomatic subjects by CT and plain mylography. Degenerate discs, bulging discs and even herniated discs are part of the aging process for the spine and may be irrelevant findings; they are seen on imaging tests of the lumbar spine in a significant percentage of subjects with no history of low back problems. Therefore, abnormal imaging findings seen in a patient with acute low back problems may not be related to that individual's symptoms.
Studies of human spinal motion have dated back to the early 19th century. Kinematic measures are attractive since a kinematic abnormality may reflect underlying pathology. Patients may avoid certain postures that cause pain and muscle activation or coactivation may provide a summary of the trunk neuromuscular status. Hand held devices such as bubble goniometers, pendulum goniometers, inclinometers and so called spondylometers have been used to measure angular differences between points on the back. However, the variability of the measurements is high. Others have made functional assessments, traditionally based on ROM measures or dynamometric measures of strength based on isometric, isokinetic, or isoinertial principles. The principal drawback of recording dynamometric data in this fashion is that successive repetition times are often unequal, thereby prohibiting averaging of repetitions, as well as generating an excessive amount of data. Subsequent interpretation and quantification of trunk dynamometric data is, therefore, often limited to examination of peak and average values of the time-varying data and is, thus, much less clinically relevant since the kinematic movement "patterns" can only be qualitatively (visually) assessed and not truly quantified to allow easy and reproducible comparisons and association with a pathology. Using such information (peak and average values) for diagnostic purposes seems to be of limited value. While lack of strength may be associated with back pain, it gives little information about the underlying diagnosis. While useful and quite easy in population studies, the use of this "quantitative" approach is not easily adaptable to the individual patient. Nevertheless, it may be interesting to assess the efficacy of various treatment modalities for low back pain.
Another drawback to traditional dynametric tests is that maximum effort may not be appropriate for all low back pain patients. Preferred motion generated by physiological submaximal effort is much more comfortable for patients and may reveal details of motion that are masked by higher levels of effort. Preferred motion has been found to be equally consistent to maximum effort low-back movement, and it is possible to predict maximum effort velocities from a knowledge of preferred effort velocities.
An interesting and innovative approach would be to conduct a more "qualitative" study, i.e. one which would examine abnormal movement patterns and profiles and enable their quantification in a reliable fashion. Such research should determine how spinal pathologies modify patterns of motion, and help to identify a "spinal signature" associated with the pathology. The functional based impairment evaluation schemes have traditionally used spinal mobility. Given the poor reliability of range of motion (ROM), its large variability among individuals, and the static psychometric nature of ROM, the use of continuous dynamic profiles of motion with the higher order derivatives has been suggested by others. A marked improvement over the use of ROM has been achieved by preserving information in the continuous profiles. As velocity appears to be a very sensitive variable in low back pain, the study of continuous velocity profiles seems promising. Preliminary studies suggest that certain conditions, such as permanent or transient spinal stenosis or marked bulging disk, are associated with specific movement patterns.
Another drawback of those dynametric measures is that they require heavy and expensive equipment. It has been shown by others that velocity is the most sensitive variable in low back condition, much more sensitive than isometric or dynametric strength. Therefore, it appears that lighter, cheaper equipment measuring displacement and velocity could be sufficient in assessing different patterns of movement associated with different clinical and pathological presentations.